{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Plot different SVM classifiers in the iris dataset\n",
    "# 使用sklearn的SVM接口实现鸢尾花分类\n",
    "\n",
    "\n",
    "Comparison of different linear SVM classifiers on a 2D projection of the iris\n",
    "dataset. We only consider the first 2 features of this dataset:\n",
    "\n",
    "- Sepal length\n",
    "- Sepal width\n",
    "\n",
    "This example shows how to plot the decision surface for four SVM classifiers\n",
    "with different kernels.\n",
    "\n",
    "The linear models ``LinearSVC()`` and ``SVC(kernel='linear')`` yield slightly\n",
    "different decision boundaries. This can be a consequence of the following\n",
    "differences:\n",
    "\n",
    "- ``LinearSVC`` minimizes the squared hinge loss while ``SVC`` minimizes the\n",
    "  regular hinge loss.\n",
    "\n",
    "- ``LinearSVC`` uses the One-vs-All (also known as One-vs-Rest) multiclass\n",
    "  reduction while ``SVC`` uses the One-vs-One multiclass reduction.\n",
    "\n",
    "Both linear models have linear decision boundaries (intersecting hyperplanes)\n",
    "while the non-linear kernel models (polynomial or Gaussian RBF) have more\n",
    "flexible non-linear decision boundaries with shapes that depend on the kind of\n",
    "kernel and its parameters.\n",
    "\n",
    ".. NOTE:: while plotting the decision function of classifiers for toy 2D\n",
    "   datasets can help get an intuitive understanding of their respective\n",
    "   expressive power, be aware that those intuitions don't always generalize to\n",
    "   more realistic high-dimensional problems.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import svm, datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_meshgrid(x, y, h=.02):\n",
    "    \"\"\"Create a mesh of points to plot in\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    x: data to base x-axis meshgrid on\n",
    "    y: data to base y-axis meshgrid on\n",
    "    h: stepsize for meshgrid, optional\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    xx, yy : ndarray\n",
    "    \"\"\"\n",
    "    x_min, x_max = x.min() - 1, x.max() + 1\n",
    "    y_min, y_max = y.min() - 1, y.max() + 1\n",
    "    xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n",
    "                         np.arange(y_min, y_max, h))\n",
    "    return xx, yy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_contours(ax, clf, xx, yy, **params):\n",
    "    \"\"\"Plot the decision boundaries for a classifier.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    ax: matplotlib axes object\n",
    "    clf: a classifier\n",
    "    xx: meshgrid ndarray\n",
    "    yy: meshgrid ndarray\n",
    "    params: dictionary of params to pass to contourf, optional\n",
    "    \"\"\"\n",
    "    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "    Z = Z.reshape(xx.shape)\n",
    "    out = ax.contourf(xx, yy, Z, **params)\n",
    "    return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import some data to play with\n",
    "iris = datasets.load_iris()\n",
    "# Take the first two features. We could avoid this by using a two-dim dataset\n",
    "X = iris.data[:, :2]\n",
    "y = iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we create an instance of SVM and fit out data. We do not scale our\n",
    "# data since we want to plot the support vectors\n",
    "C = 1.0  # SVM regularization parameter\n",
    "models = (svm.SVC(kernel='linear', C=C),\n",
    "          svm.LinearSVC(C=C, max_iter=10000),\n",
    "          svm.SVC(kernel='rbf', gamma=0.7, C=C),\n",
    "          svm.SVC(kernel='poly', degree=3, gamma='auto', C=C))\n",
    "models = (clf.fit(X, y) for clf in models)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# title for the plots\n",
    "titles = ('SVC with linear kernel',\n",
    "          'LinearSVC (linear kernel)',\n",
    "          'SVC with RBF kernel',\n",
    "          'SVC with polynomial (degree 3) kernel')\n",
    "\n",
    "# Set-up 2x2 grid for plotting.\n",
    "fig, sub = plt.subplots(2, 2)\n",
    "plt.subplots_adjust(wspace=0.4, hspace=0.4)\n",
    "\n",
    "X0, X1 = X[:, 0], X[:, 1]\n",
    "xx, yy = make_meshgrid(X0, X1)\n",
    "\n",
    "for clf, title, ax in zip(models, titles, sub.flatten()):\n",
    "    plot_contours(ax, clf, xx, yy,\n",
    "                  cmap=plt.cm.coolwarm, alpha=0.8)\n",
    "    ax.scatter(X0, X1, c=y, cmap=plt.cm.coolwarm, s=20, edgecolors='k')\n",
    "    ax.set_xlim(xx.min(), xx.max())\n",
    "    ax.set_ylim(yy.min(), yy.max())\n",
    "    ax.set_xlabel('Sepal length')\n",
    "    ax.set_ylabel('Sepal width')\n",
    "    ax.set_xticks(())\n",
    "    ax.set_yticks(())\n",
    "    ax.set_title(title)\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.2"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": false,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
